Biography
Shuvodeep De is a Lecturer and Research Scientist in the Ingram School of Engineering at Texas State University, where his work bridges computational engineering, artificial intelligence, advanced manufacturing, and engineering education. He received his Bachelor in Mechanical Engineering from Jadavpur University (India) and his Ph.D. in Computational Mechanics from Virginia Tech, with doctoral research focused on Multidisciplinary Design Optimization (MDO) and HPC Applications.
Before joining Texas State, he held research positions at Oak Ridge National Laboratory (ORNL). At ORNL, he worked in a multidisciplinary high-performance computing environment on advanced manufacturing process simulation and large-scale scientific computing, including leadership-class computing systems.
His current interests increasingly center on Scientific AI: combining machine learning, physics-based simulation, optimization, and autonomous AI agents to accelerate materials discovery, engineering design, and scientific workflows. At Texas State, he also develops hands-on educational experiences in digital manufacturing, CAD/CAM, applied AI, and robotics, with an emphasis on connecting computational tools to real engineering systems.
Before joining Texas State, he held research positions at Oak Ridge National Laboratory (ORNL). At ORNL, he worked in a multidisciplinary high-performance computing environment on advanced manufacturing process simulation and large-scale scientific computing, including leadership-class computing systems.
His current interests increasingly center on Scientific AI: combining machine learning, physics-based simulation, optimization, and autonomous AI agents to accelerate materials discovery, engineering design, and scientific workflows. At Texas State, he also develops hands-on educational experiences in digital manufacturing, CAD/CAM, applied AI, and robotics, with an emphasis on connecting computational tools to real engineering systems.
Research Interests
AI for scientific discovery and engineering design, including materials discovery, physics-informed and surrogate modeling, active learning, Bayesian and evolutionary optimization, generative design, and autonomous computational workflows. A growing focus is the development of AI agents for scientific applications that can reason across simulation, data analysis, optimization, literature, and engineering software. His broader research spans additive manufacturing, nonlinear mechanics, multiphysics simulation, and computational materials engineering.
Teaching Interests
Digital Manufacturing, CAD/CAM, Applied Artificial Intelligence, Robotics, Finite Element Analysis, Mechanics and Materials, Design Optimization, and project-based engineering education integrating simulation, computation, and physical fabrication.